GazeRayCursor: Facilitating Virtual Reality Target Selection by Blending Gaze and Controller Raycasting
Bibliographic record
Abstract
Raycasting is a common method for target selection in virtual reality (VR). However, it results in selection ambiguity whenever a ray intersects multiple targets that are located at different depths. To resolve these ambiguities, we estimate object depth by projecting the closest intersection between the gaze and controller rays onto the controller ray. An evaluation of this method found that it significantly outperformed a previous eye convergence depth estimation technique. Based on these results, we developed GazeRayCursor, a novel selection technique that enhances Raycasting, by leveraging gaze for object depth estimation. In a second study, we compared two variations of GazeRayCursor with RayCursor, a recent technique developed for a similar purpose, in a dense target environment. The results indicated that GazeRayCursor decreased selection time by 45.0% and reduced manual depth adjustments by a factor of 10 in a dense target environment. Our findings showed that GazeRayCursor is an effective method for target disambiguation in VR selection without incurring extra effort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".